The digital world is loaded with data. So much that if you want to use these data to make some sensible insights and forecasts, you need to have not only advanced mechanism as BI tools, but you would also need to bifurcate the right data from the muddled ocean of numbers and strings. These data should be complete and sufficient to project results, which can support decision-making.
To add to the complexity, every organization’s data grow manifolds with time.
What is Analytics Maturity Curve?
With the amount of data each organization is collecting, data can be analyzed to reveal a lot about the past, present, and future of the business. At what stage is the organization reaping the benefits from data defines its maturity in analytics.
“With data come out, insights, and with insights come out decisions.
Insights and decisions are the two main targets in data synthesis. Decisions also require foresights, although not it isn't mandatory. These two steps make the analytics lifecycle. Typically, the analytics maturity curve passes from 4 stages based on the level of automation plied.
Descriptive Analytics tells ‘what happened?’. The simplest kind of scrutiny running on the raw data to summarize the basic results from the past like sales in the last quarter, customer churn rate in the last month, daily social media hits. Google Analytics is a popular descriptive analytics tool. The level helps to get the answers of simple questions and includes legacy tools like excel reports, OLAP reporting, data extraction using SQL scripts.
Diagnostic Analytics tells ‘why did it happen?’ with data being massaged and reported. Such a stage is the second stage of maturity in data. In diagnostics analytics, Business Intelligence tools are employed to slice and dice, drilling down to the granular levels, and data mining to some extent to find why some incidents happened that influenced the business. A few instances are customer sentiments analysis, system failure logs analysis, and finding the root cause of staggering sales.
Predictive Analytics tells ‘what will happen?’. When the level of analytics can forecast views about likely forthcoming events with collected data, the stage is called predictive analytics. The organization can predict potential outcomes with their initiatives and thus, land much higher in the maturity curve as compared to their peers.
Fraud analysis, market sentiment analysis, the likelihood of customer churn are some foresights. Such analytics can be drawn out of the interpolation of data from the past to forecast.
Prescriptive Analytics tells ‘how can we do it?’. As the name advocates, the businesses can determine action items for the current situation or tactical and strategic decisions for the coming days. This stage heavily relies on predicted outcomes. Based on a prediction, an action should trigger. This is the final and most complex stage in the data analytics maturity model.
An organization rich with nifty data and cutting-edge analytics technologies with AI can evaluate the methods it should deploy in their processes to gain the desired outcome. The intelligence used in the 3rd stage drives the actions and decisions. If you know the expected customer churn in the forthcoming quarter, you can easily roll out a plan to retain and attract customers.
What can be Expected at Each Stage of the Analytics Landscape?
Gartner gathers from one of its surveys that more than 87% of the businesses are in the basic and opportunistic categories.
Organizations still using excel sheets for analyzing data are at the base of the maturity of the analytics curve. They are still stuck to the legacy system and just able to infer, what is happening in the organization. So they are in the zone of descriptive analytics.
Businesses invested in business intelligence tools with the aid of data warehousing architecture are a step ahead into the opportunistic zone. They are able to identify the reasons for past incidents and current business health. They leverage the power of various BI tools but not advanced enough to envision the future and hence are in the diagnostic zone. Data they use are structured, cleansed, and compatible together, and help make a good understanding of the business standing.
What can Take You Towards the Higher End of the Curve?
For these companies, the reasons for not moving ahead in the maturity model are various. Data handling expertise is not available in-house, the budget is constrained, and the system is confined to legacy rules, to name a few. Such firms also struggle to handle data quality issues. More often, their departments also work in silos. Limitations in the vision of the executives to reap the benefits of the advanced technologies available in the market is another issue.
Worry not for your data handling; this can be easily resolved with a consultation with analytics experts like Logesys Technologies.
When we move ahead in the maturity lifecycle of data, the comparatively matured organizations are employing various techniques to arrive at the predictions of the organizational performance. The departments work in collaboration with a single data repository or connected data marts to get one view about the facts and numbers. They are also vision clear on what they want to achieve. Their roadmap with goals, milestones, and performance measurement techniques augmented with the brilliance of the BI technologies garner them the medal of data matured organizations.
The organizations with mastery over predictive analytics have centralized data repository with the enterprise-wide approach. The analytics technologies are driven by smaller but concrete targets with specialists in analytics areas using Machine learning and statistics.
Comparatively, the matured organizations, according to the curve, have highly massaged and sufficient data to leverage their insights and foresights. They invest in the data quality and enterprise-wide architecture in the system. The departments work in collaboration, and the analysts are experienced with skills in ML and AI. These matured data objectives drive them to be top-notch and hence in the prescriptive analytics area. Such organizations rely on the usage of data science and embed powerful languages like R and Python with data to generate a picturesque forecast.
One thing very interesting to note about the life cycle of data is, the more you move towards the advanced landscape, the lesser would be human efforts. Automation takes care of human efforts and generates better consumable foresights for tactical and strategic decision-making for the businesses.
Data science is one area that focuses on achieving long-term visions and dwells in the matured zones, viz. predictive and prominently prescriptive data analytics.
For you to attain business maturity in data analytics landscape, not only investment is needed in advanced BI and occasionally data science, your data should be compliant across the departments and compel minimal redundancy. Additionally, the repository should be sufficient to be swallowed by the technologies to create a long-term decision-making system.